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Record W7133048226

Photodynamic Therapy During In Vivo Lung Perfusion for Treatment of Lung Metastases

2023· dissertation· W7133048226 on OpenAlexaff
Khaled Tarek Ramadan

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotodynamic therapyLungIn vivoPerfusionLung cancerPhotosensitizer
DOInot available

Abstract

fetched live from OpenAlex

Isolated lung metastases in sarcoma and colorectal cancer patients are inadequately treated with current standard therapies. In Vivo Lung Perfusion, a novel platform, could overcome limitations to photodynamic therapy treatment volumes by using low cellular perfusate, removing blood, and thus theoretically allowing greater light penetration. Development of personalized photodynamic therapy protocols requires in silico light propagation simulations based on optical properties and maximal permissible photodynamic threshold doses of lung tissue. This approach aims to maximize the effective treatment dose to the lung while avoiding toxicity to healthy lung tissue. Based on this rationale, the overall objective of this thesis is to create a whole-lung perfusion assisted PDT protocol for the treatment of lung metastases demonstrating adequate safety and feasibility to guide clinical translation. To achieve this goal, the first aim is to quantify key biophysical properties necessary for PDT; specifically, the optical properties for blood and low-cellular perfusion and the photodynamic threshold dose for 5-ALA and Chlorin e6. This will demonstrate the difference in light penetration for low cellular perfusate vs. blood. The second aim of this work is to develop a 72-hr porcine In Vivo Lung Perfusion survival model to allow assessment of acute and delayed lung toxicity. This model is validated using an accelerated titration dose-escalation study of delivery of oxaliplatin chemotherapy. The final aim of this work involves combining the previous aims to develop a full treatment protocol. Firstly, a light delivery system is created that can homogenously deliver light to the entire lung, and using Monte Carlo simulation software, can be used to simulate personalized treatment plans. The safety and feasibility of a full treatment protocol of whole-lung perfusion-assisted photodynamic therapy is then demonstrated, examining the maximally tolerated doses of 5-ALA and Chlorin e6 photosensitizers. Overall, the protocols and knowledge from this work will provide the basis for Phase 1 clinical trials assessing the safety of whole-lung perfusion assisted PDT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.400
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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